Learn AI Together — Towards AI Community Newsletter #5

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week’s podcast episode is a must-listen and stands out as the best among all 24 episodes so far. Greg shares incredible insights relevant not only for entrepreneurs but for anyone.

We talked about investments, startups, and Greg’s notable success on LinkedIn, where he has built a massive following of over 190,000 people and earned 2 top voice LinkedIn awards. He shared how and why he started his LinkedIn journey, building such a massive audience.

My personal favorite is the tips he provides for aspiring entrepreneurs and investors. The insights, covering topics like understanding the market and avoiding common pitfalls, are invaluable. Whether you’re looking to create your own products, build projects, or invest your money in other companies (which is advisable if you can), this episode is a must-listen for you.

I hope you enjoy it, and I’d love to hear your feedback and thoughts on Greg’s perspective. Feel free to share your comments or message me on social media.

Enjoy the rest of the newsletter with amazing opportunities from the community and, once again, some great reads! I enjoyed Youssef Hosni’s overview of the best weekly papers in LLMs and computer vision.

P.s. Even though not discussed here, I would highly recommend anyone interested in LLMs to add HuggingFace’s new blog post, “Mixture of Experts Explained,” to your reading list to better understand how GPT-4 and all recent powerful models are trained and deployed!

– Louis-François Bouchard, Towards AI Co-founder and CTO

What’s AI Weekly

In this week’s What’s AI Podcast episode, Louis Bouchard interviewed Greg Coquillo, 2-times LinkedIn top voice, senior product manager, and avid AI startup advisor and investor. Greg shares insights on identifying crucial market signals that guide startups on pivoting or persevering. They also explore common pitfalls that AI startups face, such as rapidly scaling and misaligning within teams. Greg’s perspective on the current startup investment landscape and the exciting potential of AI-enabled solutions is particularly compelling. This episode is highly relevant for entrepreneurs, investors, and anyone interested in the dynamic world of startups and AI innovation. If you are interested in the startup ecosystem, especially in the AI domain, tune in to Spotify, Apple Podcasts, or YouTube!

Learn AI Together Community section!

Featured Community post from the Discord

Kordcampbell built Mitta AI, a pipeline management system for large-scale document and data processing. The framework can be used to construct customized RAG-based pipelines. It also supports calls to OpenAI and Google’s Document and Vision APIs and calls to other machine learning models. Check it out here and support a fellow community member. Share your thoughts and feedback in the thread!

AI poll of the week!

Newsletters, YouTube, and Communities seem to be helping the most with staying updated with AI. For us, too 🙂

Tag and share your favorite resources in the thread and join the conversation on Discord.

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Marshallshaddy is currently looking for individuals to join their team in three key areas: Technical Writing, Frontend Development, and Backend Development. If you want to be a part of AI projects with real-world applications, connect with them in the thread.
2. JaggedGem wants to create and train an AI model that can take an image’s outline as input and produce a series of mathematical equations as output. When these equations are plotted, they should create the outline of the original image. If you can help with an AI “Image2Graph” model, reach out to them in the thread.
3. Akeshav is starting their ML journey and wants to dive deeper into maths and stats. Currently, the focus is on research and projects. If you are on a similar journey, join the conversation in the thread.

Meme of the week!

Meme shared by rucha8062

TAI Curated section

Article of the week

Run Local LLM Inference10x Faster (244 TOK/s): PyTorch II by Dr. Mandar Karhade

Power up your LLM inference with cutting-edge PyTorch techniques that promise a substantial speed boost. In the dynamic realm of natural language processing, efficient model inference is key. The latest advancements by the PyTorch team showcase a suite of optimization methods that propel local LLM inference speeds by a remarkable tenfold.

Our must-read articles

1.Top Important Computer Vision Papers for the Week from 27/11 to 03/12 by Youssef Hosni

The article reviews key computer vision works. Explore the latest computer vision research (27/11–03/12). Stay updated with groundbreaking studies in image recognition and video analysis, shaping the future of machine vision. Explore ‘VideoBooth,’ a study on improving text-driven video creation with image prompts for more precise, customized content.

2. A Comprehensive Guide to Stakeholder Analysis in AI Governance (Part 2) by Lye Jia Jun

Understanding AI governance involves recognizing the varied stakeholders involved, each with its own influence and interests within the dynamic AI landscape.International/government bodies impact AI regulation globally, while industry groups shape key standards and AI safety practices. Academic institutions drive AI knowledge and inform policy, influencing the field’s direction without regulation. Meanwhile, users and ethicists ensure AI’s ethical use, prioritizing humanity in tech progress.

3. Top Important LLM Papers for the Week from 27/11 to 03/12 by Youssef Hosni

Stay updated on AI advances with the latest LLM research, offering in-depth analysis of significant papers. The selected papers explore model optimization, scaling art, and performance refinement of LLMs, clarifying their current state and future direction.

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Published via Towards AI